Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917897059696640 |
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| author | Zhang, Huichao Wang, Pengyu Li, Manyi Li, Zuojun Wu, Yaguang |
| author_facet | Zhang, Huichao Wang, Pengyu Li, Manyi Li, Zuojun Wu, Yaguang |
| contents | We present the Unit Region Encoding of floorplans, which is a unified and compact geometry-aware encoding representation for various applications, ranging from interior space planning, floorplan metric learning to floorplan generation tasks. The floorplans are represented as the latent encodings on a set of boundary-adaptive unit region partition based on the clustering of the proposed geometry-aware density map. The latent encodings are extracted by a trained network (URE-Net) from the input dense density map and other available semantic maps. Compared to the over-segmented rasterized images and the room-level graph structures, our representation can be flexibly adapted to different applications with the sliced unit regions while achieving higher accuracy performance and better visual quality. We conduct a variety of experiments and compare to the state-of-the-art methods on the aforementioned applications to validate the superiority of our representation, as well as extensive ablation studies to demonstrate the effect of our slicing choices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications Zhang, Huichao Wang, Pengyu Li, Manyi Li, Zuojun Wu, Yaguang Computer Vision and Pattern Recognition We present the Unit Region Encoding of floorplans, which is a unified and compact geometry-aware encoding representation for various applications, ranging from interior space planning, floorplan metric learning to floorplan generation tasks. The floorplans are represented as the latent encodings on a set of boundary-adaptive unit region partition based on the clustering of the proposed geometry-aware density map. The latent encodings are extracted by a trained network (URE-Net) from the input dense density map and other available semantic maps. Compared to the over-segmented rasterized images and the room-level graph structures, our representation can be flexibly adapted to different applications with the sliced unit regions while achieving higher accuracy performance and better visual quality. We conduct a variety of experiments and compare to the state-of-the-art methods on the aforementioned applications to validate the superiority of our representation, as well as extensive ablation studies to demonstrate the effect of our slicing choices. |
| title | Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.11097 |